差分量子粒子群算法的分数阶混沌系统参数估计

doi:10.16182/j.issn1004731x.joss.17-0265

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (8): 1664-1673.doi: 10.16182/j.issn1004731x.joss.17-0265

差分量子粒子群算法的分数阶混沌系统参数估计

董泽¹, 马宁^1,2

1.华北电力大学河北省发电过程仿真与优化控制工程技术研究中心,保定 071003;
2.华北电力大学控制与计算机工程学院北京 102206

收稿日期:2017-06-06 修回日期:2017-09-20 发布日期:2019-12-12
作者简介:董泽(1970),男,河北保定,博士,教授,博导,研究方向为大型火电机组自动化、智能控制。
基金资助:
国家自然科学基金(2015BJ0030),山西省煤基重点科技攻关项目(MD2014-03-06-02)

Differential Evolution Quantum Particle Swarm Optimization for Parameter Estimation of Fractional-order Chaotic System

Dong Ze¹, Ma Ning^1,2

1. Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China;;
2. School of Control and Computer Engineering,North China Electric Power University, Beijing 102206, China

Received:2017-06-06 Revised:2017-09-20 Published:2019-12-12

摘要/Abstract

摘要： 为了精确估计分数阶混沌系统的未知参数,提出一种基于差分特征的量子粒子群优化算法：在量子粒子群算法基础上引入变异交叉选择操作,增加种群变化的多样性,提高对个体极值信息的利用水平,避免粒子后期陷入局部最优;利用多邻域局部搜索策略提高算法搜索精度。将所提算法用于求解5个测试函数,取得了良好的搜索效果。以分数阶Lorenz混沌系统和分数阶Chen混沌系统作为辨识对象,利用本文所提算法进行未知参数估计,估计结果表明本文算法具有优良的有效性和鲁棒性。

关键词: 分数阶混沌系统, 参数估计, 量子粒子群算法, 差分进化, 多邻域搜索

Abstract: In order to accurately estimate the unknown parameters for fractional order chaotic systems, a quantum particle swarm optimization algorithm based on differential quantum properties is proposed. On the basis of quantum behaved particle swarm optimization, variation, crossover and selection operation are utilized by particles, which can better keep the diversity of the particles in the population, avoiding the local optimum in the later phase of the iteration. The multi-neighborhood local search strategy is used for particles’ local search to improve search precision. Standard test functions are used to test the algorithm, and the test results show that the algorithm has good global search capability. At last, the proposed algorithm is applied in the parameter estimation for fractional-order Lorenz system and fractional-order Chen system, and the estimation results demonstrate that the algorithm is effective and robust.

Key words: fractional-order chaotic systems, parameter estimation, quantum particle swarm optimization, differential evolution, multi-neighborhood search

中图分类号:

TP273

董泽, 马宁. 差分量子粒子群算法的分数阶混沌系统参数估计[J]. 系统仿真学报, 2019, 31(8): 1664-1673.

Dong Ze, Ma Ning. Differential Evolution Quantum Particle Swarm Optimization for Parameter Estimation of Fractional-order Chaotic System[J]. Journal of System Simulation, 2019, 31(8): 1664-1673.

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差分量子粒子群算法的分数阶混沌系统参数估计

Differential Evolution Quantum Particle Swarm Optimization for Parameter Estimation of Fractional-order Chaotic System

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